LAB/IN VITRO RESEARCH

e-ISSN 1643-3750 © Med Sci Monit, 2019; 25: 3750-3761 DOI: 10.12659/MSM.913918

Received: 2018.11.01 Accepted: 2019.01.24 MMP20 Single-Nucleotide Polymorphisms Published: 2019.05.20 Correlate with Susceptibility to Alcohol-Induced Osteonecrosis of the Femoral Head in Chinese Males

Authors’ Contribution: E 1,2 Feimeng An* 1 Inner Mongolia Medical University, Hohhot, Inner Mongolia, P.R. China Study Design A E 3 Jieli Du* 2 Department of Trauma Orthopedics, The Second Affiliated Hospital of Inner Data Collection B Mongolia Medical University, Hohhot, Inner Mongolia, P.R. China Statistical Analysis C B 1,2 Jiaqi Wang 3 Department of Orthopedics, Cangzhou People’s Hospital, Cangzhou, Hebei, Data Interpretation D C 3 Liang Zhao P.R. China Manuscript Preparation E F 2 Chao Ma Literature Search F Funds Collection G B 2 Jian Zhao AG 2 Jianzhong Wang

* Feimeng An and Jieli Du are co-first author Corresponding Author: Jianzhong Wang, e-mail: [email protected] Source of support: This work was supported by the National Natural Science Foundation of China (No. 81160228, 81260284, 81660378)

Background: Alcohol-induced osteonecrosis of the femoral head (ONFH) is caused by the interaction of genetic and environ- mental factors. Genetic variations of matrix metalloproteinase (MMP) system are associated with ONFH de- velopment and progression. In this study, we aimed to evaluate the relationships between MMP20 gene poly- morphisms and the risk of alcohol-induced ONFH in Chinese Han males. Material/Methods: In this case-control study, genotypes of 14 selected SNPs in the MMP20 gene were assayed using MassARRAY in 299 male cases with alcohol-induced ONFH and in 197 healthy males. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the influence of gene polymorphism on occurrence of alcohol-induced ONFH by allelic model analysis, genotype model analysis and haplotype analysis. Results: After allelic model analysis, the minimum alleles of rs10895322, rs1784424, rs3781788, and rs1573954 corre- lated with an increased risk of alcohol-induced ONFH (P<0.05). Genetic model analysis revealed significant as- sociations of 9 SNPs with alcohol-induced ONFH occurrence even after adjustment for age (P<0.05): 2 protec- tive SNPs (rs1711423 and rs1784418) and 7 high-risk SNPs (rs10895322, rs1784424, rs3781788, rs7126560, rs1573954, rs1711399, and rs2292730). Moreover, 8 SNPs showed a statistically significant association with different clinical phenotypes (P<0.05). Beyond that, haplotype “CGGTTCCA” in MMP20 was discovered to cor- relate with a 1.63-fold increased risk of alcohol-induced ONFH (OR: 1.63, 95% CI: 1.15–2.30, P=0.0058). Conclusions: Our data sheds new light on the associations of MMP20 gene polymorphisms with alcohol-induced ONFH pre- disposition in Chinese Han males.

MeSH Keywords: Genetic Association Studies • Matrix Metalloproteinase 20 • Osteonecrosis • Polymorphism, Genetic

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Background and lung A549 cell lines. Thus, they concluded that MMP20 was a potential new candidate for tumor diagnosis or therapy. Alcohol-induced osteonecrosis of the femoral head (alcohol- induced ONFH) caused by excessive and chronic alcohol con- In this case-control study, 14 SNPs on the MMP20 gene were sumption is an intractable orthopedic disease that is charac- selected to shed light on possible correlations with the occur- terized by the progressive collapse of the femur head and loss rence of alcohol-induced ONFH and the clinical phenotypes in of hip joint function. Although there are a variety of clinical a Chinese male population. treatments, the prevalence of alcohol-induced ONFH and result- ing disability in the Chinese population continues to increase, particularly among young men. Data demonstrate that 30.7% Material and Methods of ONFH cases in China are alcohol induced [1]. For these rea- sons, identifying risk factors and taking primary prevention Study participants measures are necessary to prevent alcohol-induced ONFH. There were 496 males (299 patients with alcohol-induced Recent research into the molecular biology and genetic under- ONFH and 197 healthy males) among Han Chinese, sequen- pinnings of alcohol-induced ONFH has provided new informa- tially enrolled in this study. All of the patients were selected tion about gene polymorphisms, which has opened up novel randomly from the Zhengzhou Traditional Chinese Medicine avenues of research to uncover the risk factors of this condi- Traumatology Hospital without restriction of disease severity. tion [2]. In the past few years, genome-wide association stud- The patients were diagnosed with ONFH after using plain radio- ies (GWAS) have identified several susceptibility genes such graphs in stage II, III, and IV of the Ficat Classification system. as APOA1, PAI-1, SREBF1, VEGF MTHFR, and TFPI that are as- Patients with traumatic osteonecrosis and other hip diseases sociated with ONFH [3–8]. Chondrocytes from a femoral head were excluded. Alcohol-induced osteonecrosis was defined with osteonecrosis lose their phenotype during ONFH, revealed by a history of consumption of more than 400 mL of alcohol by reducing of extracellular matrix production in glycosami- per week [18]. As well, enrolled patients had not received sys- noglycan (GAG) and type 2 collagen excretion [9]. In addition, temic treatment before the blood samples used for the study our previous studies demonstrated that NOS3, ABCB, IL23R, were collected. Control study participants were genetically ir- APOA1, APOB, TIMP2, MMP2, MMP3, and MMP8 single-nucle- relevant healthy males who were enrolled from Zhengzhou otide polymorphisms were related to the risk of alcohol-in- Medical Center and lived in or near Zhengzhou without hip duced ONFH and clinical outcomes or other clinical charac- pain, without lesions found on hip joint plain radiographs, and teristics in the Chinese population [10–13]. Although multiple without relationship to the enrolled patients. Those who had gene variants have been proposed as risk factors for alcohol- a chronic metabolic disorder or who needed steroid treatment induced ONFH, the molecular etiology and pathogenesis have were also excluded from this study. remained indistinct. A normative questionnaire was used to collect personal in- The matrix metalloproteinase-20 (MMP20) gene is located in formation. Our research was approved by the ethics commit- an MMP cluster with 7 other MMPs at chromosomal location tee of Zhengzhou Traditional Chinese Medicine Traumatology 11q22.3. MMP20, which has been well established as an es- Hospital and The Second Affiliated Hospital of Inner Mongolia sential participant in amelogenesis, is expressed in amelo- Medical University (No. YKD2016138). Signed informed con- blasts and odontoblasts during dental enamel development sent documents were obtained from all candidates. and is responsible for cleaving and removing most of the pro- tein components of the extracellular enamel matrix such as SNP selection and genotyping amelogenin (the most abundant extracellular matrix (ECM) protein in developing enamel) thus facilitating enamel miner- A total of 14 SNPs were selected from the NCBI dbSNP (http:// alization [14,15]. MMP20 is a relatively newly identified mem- www.ncbi.nlm.nih.gov/snp) and the 1000 Genomes Project da- ber of the MMP family and was also named “human enamely- tabases (http://www.internationalgenome.org/) with minor al- sin” because it was originally thought that MMP20 expression lele frequencies (MAFs) >5% in the 1000 Genomes Chinese Han was restricted to enamel. In recent years, the expression of Beijing population. Genomic DNA was extracted from the pe- MMP20 has also been reported in other tissues. A study in ripheral blood of the participants using the GoldMag whole 2015 confirmed the expression of MMP20 in the retina and blood genomic DNA purification kit (GoldMag Co. Ltd., Xi’an, RPE/choroid [16]. Kraus et al. [17]investigated the expression China). DNA concentration was determined using a NanoDrop of MMP20 in 3 major human tumor entities: colon, breast, and 2000C spectrophotometer (Thermo Scientific, Waltham, MA, lung tumors. They found that MMP20 was identified at both USA). Primers for amplification process and single base ex- the mRNA and the protein level in breast MCF-7, colon HT-29, tension reactions were designed using the Assay Design Suite

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Table 1. Characteristics of the male individuals in controls and alcohol-induced ONFH patients.

Cases (n=299) Controls (n=197) Variables P value N (%) N (%) Age, years Mean ±SD 43.24±13.07 49.76±8.83 P<0.001* £50 215 (71.9) 113 (57.4) >50 84 (28.1) 84 (42.6) Clinical stages Stage II 74 (24.7) Stage III 121 (40.5) Stage IV 104 (34.8) Hip lesions Unilateral 70 (23.4) Bilateral 229 (76.6) Course, months >12 151 (50.5) £12 148 (49.5)

P<0.05 indicates statistical significance. * Independent samplest -test.

V2.0 (https://agenacx.com/online-tools/). SNP genotyping was Results performed by Agena MassARRAY iPLEX (Agena Bioscience, San Diego, CA, USA). Genotyping results were output by Agena Altogether, 299 eligible cases with alcohol-induced ONFH Bioscience TYPER version 4.0 software. (mean age ±SD: 43.24±13.07) and 197 controls (mean age ±SD: 49.71±8.85) were recruited for our study. All of the in- Statistical analysis dividuals were males. The results of t-tests showed that the cases and controls were not matched for age (P<0.05), so we SPSS 19.0 (version 19.0, Chicago, IL, USA) was used to do the adjusted the age factor in the subsequent analysis (Table 1). statistical analysis. All the P-values were 2-sided, and P<0.05 was considered to be statistically significant. Thet- test were The detailed information about the 14 selected SNPs on the used to compare the age difference of cases and controls. MMP20 gene including band, alleles, minor allele frequency An exact test was used to assess the variation in each SNP gen- (MAF), and HWE results. The P-values of alleles were calcu- otype frequency from the Hardy-Weinberg equilibrium (HWE) lated by chi-square test. All SNPs did not deviate from HWE in the control participants before analysis. Differences in SNP (P>0.05). Comparing the differences in MAF distributions be- genotype and allele distribution between cases and controls tween cases and controls, we found that 4 SNPs may signifi- were compared by the c2 test/Wald test. Four models including cantly correlate with the risk of alcohol-induced ONFH under the the codominant, dominant, recessive, and log-additive model allele model (rs10895322G/A, OR=1.422, 95% CI=1.069–1.892, were used to assess the association between each genotype P=0.015; rs1784424T/G, OR=1.368, 95% CI=1.059–1.767, and the risk of alcohol-induced ONFH. Finally, both the SHEsis P=0.016; rs3781788T/C, OR=1.345, 95% CI=1.031–1.755, software platform (http://analysis.bio-x.cn/myAnalysis.php) and P=0.029; rs1573954C/T, OR=1.328, 95% CI=1.028–1.716, the Haploview software (version 4.2) were used to estimate P=0.030) (Table 2). the linkage disequilibrium (LD) patterns and construct haplo- types. The risk associated with individual genotypes and al- Subsequently, by unconditional logistic-regression analysis, we lele was calculated as the odds ratio (OR) with their 95% con- further identified the correlation between selected SNPs and fidence interval (95% CI) based on logistic regression model alcohol-induced ONFH risk under 4 gene models (codominant, analysis with an adjustment for age. dominant, recessive, and log-additive). Meanwhile, all results were adjusted by age (Table 3). Rs10895322 and rs1784424 notably increased the risk of alcohol-induced ONFH under all of the gene models, including codominant, dominant, recessive,

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Table 2. Basic information of candidate SNPs in this study.

MAF Alleles HWE b SNP ID Gene Band a ORs 95% CI P value A/B Case Control P value rs2292730 MMP20 11q22.2 A/G 0.296 0.254 0.192 1.24 0.93 1.64 0.147 rs1784410 MMP20 11q22.2 C/A 0.401 0.424 1.000 0.91 0.70 1.18 0.474 rs1711399 MMP20 11q22.2 G/T 0.462 0.418 0.559 1.19 0.92 1.54 0.181 rs1711437 MMP20 11q22.2 T/C 0.385 0.413 1.000 0.89 0.69 1.16 0.379 rs1711433 MMP20 11q22.2 C/T 0.010 0.005 1.000 1.99 0.40 9.89 0.623 rs10895322 MMP20 11q22.2 G/A 0.326 0.254 0.851 1.42 1.07 1.89 0.015* rs1784424 MMP20 11q22.2 T/G 0.575 0.497 0.775 1.37 1.06 1.77 0.016* rs3781788 MMP20 11q22.2 T/C 0.403 0.334 0.521 1.35 1.03 1.76 0.029* rs1711423 MMP20 11q22.2 G/T 0.425 0.487 1.000 0.78 0.60 1.00 0.053 rs17174327 MMP20 11q22.2 T/C 0.062 0.087 0.370 0.69 0.43 1.13 0.138 rs1784418 MMP20 11q22.2 T/C 0.428 0.487 0.670 0.79 0.61 1.02 0.067 rs7126560 MMP20 11q22.2 A/G 0.404 0.343 1.000 1.30 0.10 1.69 0.053 rs2245803 MMP20 11q22.2 G/A 0.002 0.005 1.000 0.33 0.03 3.64 0.717 rs1573954 MMP20 11q22.2 C/T 0.555 0.485 0.317 1.33 1.03 1.72 0.030*

A – minor alleles; B – major alleles; * P<0.05 indicates statistical significance;a P was calculated by exact test; b P was calculated by Pearson chi-squared test. Bold values indicate a significant difference. and log-additive. Rs3781788 also revealed an increased risk in patients with >12 months course or/and stage III of alco- under both the dominant and the additive model. Meanwhile, hol-induced ONFH. Regarding rs7126560 and rs2292730, these both rs7126560 and rs2292730 revealed an increased risk un- 2 SNPs only revealed the increased risk for >12 months course der the log-additive model, rs1573954 exhibited an increased and stage IV alcohol-induced ONFH, respectively. risk under 2 models (recessive and log-additive), and rs1711399 revealed an increased risk under the recessive model. In addi- Finally, using parameter D’, we detected the extent of link- tion, rs1711423 showed a negative effect under the recessive age disequilibrium between SNPs, and then determined the and log-additive models, and rs1784418 also showed a de- haplotype LD block according to the control group data. We creased risk under both the dominant model and the log-ad- observed that 2 blocks existed strong linkage disequilibria ditive model. in MMP20 (Figure 1). Block 1 included 8 SNPs: rs1711437, rs10895322, rs1784424, rs3781788, rs1711423, rs17174327, Next, we completed the correlation analysis of alleles and gen- rs1784418, and rs7126560; block 2 includes 2 SNPs: rs1784410 otypes of SNPs with the clinical phenotypes (unilateral or bilat- and rs11711399. In block 1, compared with the “TATCGCTG” eral hip lesions, >12 months or £12 months course, and clin- wild-type, the “CGGTTCCA” haplotype was found to be associ- ical stages of alcohol-induced ONFH), respectively (Table 4). ated with an increased alcohol-induced ONFH risk after adjust- Regarding rs10895322, some significant association was found ment for age (OR=1.63, 95% CI=1.15–2.30, P=0.0058) (Table 5). between the G allele and the patients with different clinical phe- However, we did not find any association between alcohol-in- notypes. In patients who had bilateral hip lesions, >12 months duced ONFH and the haplotype of block 2. course, and/or stage II/III alcohol-induced ONFH, the G allele was a risk factor. Likewise, the rs3781788 T allele showed the same results. Among patients who had bilateral hip lesions, Discussion >12 months course, and/or stage III alcohol-induced ONFH, the G allele of rs1784424 showed significantly increased risk, and In this study, we have attempted to show the correlation be- the G allele of rs1711423 showed significantly decreased risk, tween MMP20 polymorphisms and alcohol-induced ONFH and relative to controls. In addition, the allele distribution showed the different clinical phenotypes in Chinese Han males. Nine sus- a higher frequency of the rs1573954 C allele in the patients ceptibility SNPs in the MMP20 gene revealed a statistically sig- with unilateral hip lesions, £12 months course, and/or stage nificant association with the alcohol-induced ONFH occurrence: II alcohol-induced ONFH. Rs1784418 showed a decreased risk 2 protective SNPs (rs1711423 and rs1784418) and 7 high-risk

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Table 3. Genotypic model analysis of relationship between SNPs and alcohol-induced ONFH risk adjusted for age.

SNP ID Model Genotype Control Case OR (95% CI) Pa-value AIC BIC rs10895322 Codominant AA 109 (55.9%) 136 (46%) 1 0.018* 625.9 642.6 GA 73 (37.4%) 127 (42.9%) 1.44 (0.97–2.14) GG 13 (6.7%) 33 (11.2%) 2.47 (1.21–5.04) Dominant AA 109 (55.9%) 136 (46%) 1 0.016* 626.1 638.7 GA-GG 86 (44.1%) 160 (54%) 1.58 (1.09–2.31) Recessive AA-GA 182 (93.3%) 263 (88.8%) 1 0.029* 627.1 639.7 GG 13 (6.7%) 33 (11.2%) 2.10 (1.05–4.19) Log-additive – – – 1.51 (1.13–2.03) 0.0051* 624 636.6 rs1784424 Codominant TT 48 (24.5%) 52 (17.4%) 1 0.018* 629.8 646.6 GT 101 (51.5%) 150 (50.2%) 1.43 (0.88–2.33) GG 47 (24%) 97 (32.4%) 2.18 (1.26–3.77) Dominant TT 48 (24.5%) 52 (17.4%) 1 0.032* 631.2 643.8 GT-GG 148 (75.5%) 247 (82.6%) 1.66 (1.05–2.63) Recessive TT-GT 149 (76%) 202 (67.6%) 1 0.014* 629.9 642.5 GG 47 (24%) 97 (32.4%) 1.69 (1.10–2.58) Log-additive – – – 1.48 (1.13–1.94) 0.0046* 627.8 640.4 rs3781788 Codominant CC 89 (45.4%) 104 (34.8%) 1 0.072 632.6 649.4 CT 83 (42.4%) 149 (49.8%) 1.53 (1.02–2.29) TT 24 (12.2%) 46 (15.4%) 1.66 (0.92–2.99) Dominant CC 89 (45.4%) 104 (34.8%) 1 0.023* 630.7 643.3 CT-TT 107 (54.6%) 195 (65.2%) 1.56 (1.06–2.28) Recessive CC-CT 172 (87.8%) 253 (84.6%) 1 0.310 634.8 647.4 TT 24 (12.2%) 46 (15.4%) 1.33 (0.77–2.29) Log-additive – – – 1.35 (1.02–1.78) 0.032* 631.3 643.9 rs1711423 Codominant TT 51 (26%) 97 (32.4%) 1 0.052 631.9 648.7 GT 99 (50.5%) 150 (50.2%) 0.74 (0.48–1.16) GG 46 (23.5%) 52 (17.4%) 0.51 (0.30–0.88) Dominant TT 51 (26%) 97 (32.4%) 1 0.057 632.2 644.8 GT-GG 145 (74%) 202 (67.6%) 0.67 (0.44–1.02) Recessive TT-GT 150 (76.5%) 247 (82.6%) 1 0.042* 631.7 644.3 GG 46 (23.5%) 52 (17.4%) 0.62 (0.39–0.98) Log-additive – – – 0.72 (0.55–0.94) 0.016* 630 642.6 rs1784418 Codominant CC 50 (25.4%) 96 (32.1%) 1 0.066 633.5 650.3 CT 102 (51.8%) 150 (50.2%) 0.71 (0.45–1.10) TT 45 (22.8%) 53 (17.7%) 0.53 (0.31–0.91) Dominant CC 50 (25.4%) 96 (32.1%) 1 0.043* 632.8 645.4 CT-TT 147 (74.6%) 203 (67.9%) 0.65 (0.43–0.99) Recessive CC-CT 152 (77.2%) 246 (82.3%) 1 0.082 633.9 646.5 TT 45 (22.8%) 53 (17.7%) 0.66 (0.41–1.05) Log-additive – – – 0.73 (0.55–0.95) 0.020* 631.5 644.1

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Table 3 continued. Genotypic model analysis of relationship between SNPs and alcohol-induced ONFH risk adjusted for age.

SNP ID Model Genotype Control Case OR (95% CI) Pa-value AIC BIC rs7126560 Codominant GG 85 (43.1%) 104 (35.1%) 1 0.130 633 649.8 AG 89 (45.2%) 145 (49%) 1.35 (0.90–2.02) AA 23 (11.7%) 47 (15.9%) 1.73 (0.95–3.13) Dominant GG 85 (43.1%) 104 (35.1%) 1 0.068 631.7 644.3 AG-AA 112 (56.9%) 192 (64.9%) 1.43 (0.97–2.09) Recessive GG-AG 174 (88.3%) 249 (84.1%) 1 0.170 633.1 645.7 AA 23 (11.7%) 47 (15.9%) 1.47 (0.84–2.54) Log-additive – – – 1.32 (1.00–1.75) 0.045* 631 643.6 rs1573954 Codominant TT 48 (24.6%) 59 (19.8%) 1 0.050 628.4 645.2 TC 105 (53.9%) 147 (49.3%) 1.31 (0.81–2.11) CC 42 (21.5%) 92 (30.9%) 1.95 (1.13–3.37) Dominant TT 48 (24.6%) 59 (19.8%) 1 0.081 629.4 642 TC-CC 147 (75.4%) 239 (80.2%) 1.50 (0.95–2.36) Recessive TT-TC 153 (78.5%) 206 (69.1%) 1 0.030* 627.7 640.3 CC 42 (21.5%) 92 (30.9%) 1.61 (1.04–2.49) Log-additive – – – 1.40 (1.06–1.84) 0.015* 626.5 639.1 rs1711399 Codominant TT 64 (32.6%) 90 (30.1%) 1 0.071 631.9 648.7 GT 100 (51%) 142 (47.5%) 1.02 (0.67–1.57) GG 32 (16.3%) 67 (22.4%) 1.78 (1.02–3.10) Dominant TT 64 (32.6%) 90 (30.1%) 1 0.390 634.4 647.1 GT-GG 132 (67.3%) 209 (69.9%) 1.19 (0.80–1.79) Recessive TT-GT 164 (83.7%) 232 (77.6%) 1 0.021* 629.9 642.5 GG 32 (16.3%) 67 (22.4%) 1.75 (1.08–2.85) Log-additive – – – 1.29 (0.99–1.68) 0.063 631.7 644.3 rs2292730 Codominant GG 106 (53.8%) 140 (46.8%) 1 0.120 634.7 651.5 GA 82 (41.6%) 141 (47.2%) 1.39 (0.94–2.04) AA 9 (4.6%) 18 (6%) 1.94 (0.81–4.66) Dominant GG 106 (53.8%) 140 (46.8%) 1 0.058 633.3 645.9 GA-AA 91 (46.2%) 159 (53.2%) 1.44 (0.99–2.09) Recessive GG-GA 188 (95.4%) 281 (94%) 1 0.240 635.5 648.1 AA 9 (4.6%) 18 (6%) 1.66 (0.71–3.90) Log-additive – – – 1.39 (1.01–1.91) 0.041* 632.7 645.3

* P<0.05 indicates statistical significance.a P values were calculated by Wald test by unconditional logistic regression adjusted for age. Bold values indicate a significant difference.

SNPs (rs10895322, rs1784424, rs3781788, rs7126560, rs1573954, MMPs devastate the cartilaginous extracellular matrix (ECM) rs1711399, and rs2292730). Among these SNPs, 8 SNPs showed production during chondrogenesis [19,20] and ONFH [9]. And a statistically significant association with different clinical phe- ECM, including GAG and collagen type 2, are commodities not notypes. In particular, the haplotype “CGGTTCCA” in MMP20 was only in normal chondrocytes but also in chondrocytic differen- also founded to be correlated with a 1.63-fold increased risk of al- tiation [21]. MMP20 may inhibit the synthesis of ECM of cho- cohol-induced ONFH. As far as we know, this is the first report to drocytes in the femoral heads under osteonecrosis. But studies reveal the correlation between these loci and ONFH susceptibility. on these risk loci are relatively rare. A recent GWAS reported

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Table 4. Stratification analysis for association ofMMP20 gene polymorphism with the clinical phenotypes of alcohol-induced ONFH.

SNPs Genotype (%) Allele (%) rs2292730 GG GA AA Pa G A Pb OR 95% CI

Controls 106 (53.8) 82 (41.6) 9 (4.60) 294 (74.6) 100 (25.4)

Unilateral 32 (45.7) 32 (45.7) 6 (8.60) 0.320 96 (68.6) 44 (31.4) 0.166 1.35 0.88 2.06

Bilateral 108 (47.2) 109 (47.6) 12 (5.20) 0.190 325 (71.0) 133 (29.0) 0.232 1.20 0.89 1.63

Stage II 32 (43.2) 39 (52.7) 3 (4.00) 0.320 103 (69.6) 45 (30.4) 0.239 1.28 0.85 1.95

Stage III 66 (54.5) 51 (42.1) 4 (3.30) 0.780 183 (75.6) 59 (24.4) 0.948 0.65 1.37 0.78

Stage IV 42 (40.4) 51 (49.0) 11 (10.6) 0.023* 135 (64.9) 73 (35.1) 0.012* 1.59 1.10 2.29

>12 months 76 (50.3) 67 (44.4) 8 (5.30) 0.710 219 (72.5) 83 (27.5) 0.532 1.11 0.79 1.56

£12 months 64 (43.2) 74 (50.0) 10 (6.80) 0.043* 202 (68.2) 94 (31.8) 0.065 1.37 0.98 1.91

rs1784410 AA CA CC Pa A C Pb OR 95% CI

Controls 65 (33.0) 97 (49.2) 35 (17.8) 227 (57.6) 167 (42.4)

Unilateral 25 (35.7) 30 (42.9) 15 (21.4) 0.710 80 (57.1) 60 (42.9) 0.923 1.02 0.69 1.51

Bilateral 84 (36.8) 109 (47.8) 35 (15.3) 0.310 277 (60.7) 179 (39.3) 0.354 0.88 0.67 1.16

Stage II 25 (34.2) 32 (43.8) 16 (21.9) 0.880 82 (56.2) 64 (43.8) 0.762 1.06 0.72 1.56

Stage III 45 (37.2) 65 (53.7) 11 (9.1) 0.012* 155 (64.0) 87 (36.0) 0.108 0.76 0.55 1.06

Stage IV 39 (37.5) 42 (40.4) 23 (22.1) 0.350 120 (57.7) 88 (42.3) 0.985 1.00 0.71 1.40

>12 months 57 (38.0) 71 (47.3) 22 (14.7) 0.520 185 (61.7) 115 (38.3) 0.282 0.84 0.62 1.15

£12 months 52 (35.1) 68 (46.0) 28 (18.9) 0.570 172 (58.1) 124 (41.9) 0.897 0.98 0.72 1.33

rs1711399 TT GT GG Pa T G Pb OR 95% CI

Controls 64 (32.6) 100 (51) 32 (16.3) 228 (58.2) 164 (41.8)

Unilateral 22 (31.4) 31 (44.3) 17 (24.3) 0.230 75 (53.6) 65 (46.4) 0.346 1.20 0.82 1.78

Bilateral 68 (29.7) 111 (48.5) 50 (21.8) 0.120 247 (53.9) 211 (46.1) 0.215 1.19 0.90 1.56

Stage II 19 (25.7) 41 (55.4) 14 (18.9) 0.450 79 (53.4) 69 (46.6) 0.317 1.21 0.83 1.78

Stage III 32 (26.4) 60 (49.6) 29 (24.0) 0.013* 124 (51.2) 118 (48.8) 0.088 1.32 0.96 1.83

Stage IV 39 (37.5) 41 (39.4) 24 (23.1) 0.120 119 (57.2) 89 (42.8) 0.822 1.04 0.74 1.46

>12 months 43 (28.5) 70 (46.4) 38 (25.2) 0.088 156 (51.7) 146 (48.3) 0.087 1.30 0.96 1.76

£12 months 47 (31.8) 72 (48.6) 29 (19.6) 0.280 166 (56.1) 130 (43.9) 0.585 1.09 0.80 1.48

rs1711437 CC TC TT Pa C T Pb OR 95% CI

Controls 67 (34.4) 95 (48.7) 33 (16.9) 229 (58.7) 161 (41.3)

Unilateral 26 (37.1) 30 (42.9) 14 (20.0) 0.800 82 (58.6) 58 (41.4) 0.976 1.01 0.68 1.49

Bilateral 87 (38.7) 107 (47.6) 31 (13.8) 0.230 281 (62.4) 169 (37.6) 0.270 0.86 0.65 1.13

Stage II 28 (38.4) 33 (45.2) 12 (16.4) 0.540 89 (61.0) 57 (39.0) 0.638 0.91 0.62 1.34

Stage III 46 (38.0) 65 (53.7) 10 (8.30) 0.005* 157 (64.9) 85 (35.1) 0.123 0.77 0.55 1.07

Stage IV 39 (38.6) 39 (38.6) 23 (22.8) 0.240 117 (57.9) 85 (42.1) 0.852 1.03 0.73 1.46

>12 months 59 (39.6) 69 (46.3) 21 (14.1) 0.480 187 (62.8) 111 (37.2) 0.284 0.84 0.62 1.15

£12 months 54 (37.0) 68 (46.6) 24 (16.4) 0.440 176 (60.3) 116 (39.7) 0.682 0.94 0.69 1.28

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Table 4 continued. Stratification analysis for association ofMMP20 gene polymorphism with the clinical phenotypes of alcohol- induced ONFH.

SNPs Genotype (%) Allele (%) rs10895322 AA GA GG Pa A G Pb OR 95%CI

Controls 109 (55.9) 73 (37.4) 13 (6.70) 291 (74.6) 99 (25.4)

Unilateral 34 (49.3) 26 (37.7) 9 (13.0) 0.110 94 (68.1) 44 (31.9) 0.140 1.38 0.90 2.10

Bilateral 102 (44.9) 101 (44.5) 24 (10.6) 0.022* 305 (67.2) 149 (32.8) 0.018* 1.44 1.06 1.94

Stage II 30 (41.1) 36 (49.3) 7 (9.60) 0.049* 96 (65.8) 50 (34.2) 0.041* 1.53 1.02 2.31

Stage III 54 (45.0) 51 (42.5) 15 (12.5) 0.007* 159 (66.3) 81 (33.8) 0.024* 1.50 1.05 2.13

Stage IV 52 (50.5) 40 (38.8) 11 (10.7) 0.410 144 (69.9) 62 (30.1) 0.218 1.27 0.87 1.84

>12 months 63 (41.7) 69 (45.7) 19 (12.6) 0.016* 195 (64.6) 107 (35.4) 0.004* 1.61 1.16 2.24

£12 months 73 (50.3) 58 (40.0) 14 (9.70) 0.092 204 (70.3) 86 (29.7) 0.216 1.24 0.88 1.74 rs1784424 TT GT GG Pa T G Pb OR 95% CI

Controls 48 (24.5) 101 (51.5) 47(24.0) 197 (50.3) 195 (49.7)

Unilateral 15 (21.7) 33 (47.8) 21(30.4) 0.490 63 (45.7) 75 (54.3) 0.140 1.38 0.90 2.10

Bilateral 36 (15.7) 117 (51.1) 76(33.2) 0.006* 189 (41.3) 269 (58.7) 0.009* 1.44 1.10 1.89

Stage II 13 (17.6) 35 (47.3) 26(35.1) 0.035* 61 (41.2) 87 (58.8) 0.061 1.44 0.98 2.11

Stage III 14 (11.7) 65 (54.2) 41(34.2) 0.001* 93 (38.8) 147 (61.3) 0.005* 1.60 1.15 2.21

Stage IV 24 (23.1) 50 (48.1) 30(28.9) 0.650 98 (47.1) 110 (52.9) 0.464 1.13 0.81 1.59

>12 months 25 (16.6) 73 (48.3) 53(35.1) 0.031* 123 (40.7) 179 (59.3) 0.013* 1.47 1.09 1.99

£12 months 26 (17.7) 77 (52.4) 44(29.9) 0.060 129 (43.9) 165 (56.1) 0.098 1.29 0.95 1.75 rs3781788 CC CT TT Pa C T Pb OR 95% CI

Controls 89 (45.4) 83 (42.4) 24 (12.2) 261 (66.6) 131 (33.4)

Unilateral 27 (39.1) 29 (42.0) 13 (18.8) 0.340 83 (60.1) 55 (39.9) 0.173 1.32 0.88 1.97

Bilateral 76 (33.2) 120 (52.4) 33 (14.4) 0.028* 272 (59.4) 186 (40.6) 0.031* 1.36 1.03 1.80

Stage II 23 (31.1) 37 (50.0) 14 (18.9) 0.061 83 (56.1) 65 (43.9) 0.024* 1.56 1.06 2.30

Stage III 36 (30.0) 68 (56.7) 16 (13.3) 0.023* 140 (58.3) 100 (41.7) 0.037* 1.42 1.02 1.98

Stage IV 44 (42.3) 44 (42.3) 16 (15.4) 0.720 132 (63.5) 76 (36.5) 0.444 1.15 0.81 1.63

>12 months 50 (33.1) 74 (49.0) 27 (17.9) 0.048* 174 (57.6) 128 (42.4) 0.015* 1.47 1.07 2.00

£12 months 53 (36.0) 75 (51.0) 19 (12.9) 0.310 181 (61.6) 113 (38.4) 0.174 1.24 0.91 1.70 rs1711423 TT GT GG Pa T G Pb OR 95% CI

Controls 51 (26.0) 99 (50.5) 46 (23.5) 201 (51.3) 191 (48.7)

Unilateral 23 (32.9) 31 (44.3) 16 (22.9) 0.560 77 (55.0) 63 (45.0) 0.449 0.86 0.58 1.27

Bilateral 74 (32.3) 119 (52.0) 36 (15.7) 0.021* 267 (58.3) 191 (41.7) 0.040* 0.75 0.57 0.99

Stage II 25 (33.8) 34 (46.0) 15 (20.3) 0.140 84 (56.8) 64 (43.2) 0.255 0.80 0.55 1.17

Stage III 42 (34.7) 65 (53.7) 14 (11.6) 0.001* 149 (61.6) 93 (38.4) 0.011* 0.66 0.47 0.91

Stage IV 30 (28.9) 51 (49.0) 23 (22.1) 0.860 111 (53.4) 97 (46.6) 0.626 0.92 0.66 1.29

>12 months 52 (34.4) 75 (49.7) 24 (15.9) 0.081 179 (59.3) 123 (40.7) 0.036* 0.72 0.53 0.98

£12 months 45 (30.4) 75 (50.7) 28 (18.9) 0.120 165 (55.7) 131 (44.3) 0.245 0.84 0.62 1.13

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Table 4 continued. Stratification analysis for association ofMMP20 gene polymorphism with the clinical phenotypes of alcohol- induced ONFH.

SNPs Genotype (%) Allele (%) rs1784418 CC CT TT Pa C T Pb OR 95% CI Controls 50 (25.4) 102 (51.8) 45 (22.8) 202 (51.3) 192 (48.7) Unilateral 25 (35.7) 29 (41.4) 16 (22.9) 0.280 79 (56.4) 61 (43.6) 0.294 0.81 0.55 1.20 Bilateral 71 (31.0) 121 (52.8) 37 (16.2) 0.044* 263 (57.4) 195 (42.6) 0.072 0.78 0.60 1.02 Stage II 25 (33.8) 34 (46.0) 15 (20.3) 0.150 84 (56.8) 64 (43.2) 0.254 0.80 0.55 1.17 Stage III 40 (33.1) 67 (55.4) 14 (11.6) 0.001* 147 (60.7) 95 (39.3) 0.020* 0.68 0.49 0.94 Stage IV 31 (29.8) 49 (47.1) 24 (23.1) 0.700 111 (53.4) 97 (46.6) 0.624 0.92 0.66 1.29 >12 months 52 (34.4) 74 (49.0) 25 (16.6) 0.096 178 (58.9) 124 (41.1) 0.044* 0.73 0.54 0.99

£12 months 44 (29.7) 76 (51.4) 28 (18.9) 0.140 164 (55.4) 132 (44.6) 0.281 0.85 0.63 1.15 rs7126560 GG AG AA Pa G A Pb OR 95% CI Controls 85 (43.1) 89 (45.2) 23 (11.7) 259 (65.7) 135 (34.3) Unilateral 27 (38.6) 29 (41.4) 14 (20.0) 0.220 83 (59.3) 57 (40.7) 0.172 1.32 0.89 1.96 Bilateral 77 (34.1) 116 (51.3) 33 (14.6) 0.100 270 (59.7) 182 (40.3) 0.072 1.29 0.98 1.71 Stage II 23 (31.1) 38 (51.4) 13 (17.6) 0.089 84 (56.8) 64 (43.2) 0.053 1.46 0.99 2.15 Stage III 37 (30.6) 67 (55.4) 17 (14.1) 0.051 141 (58.3) 101 (41.7) 0.058 1.37 0.99 1.91 Stage IV 44 (43.6) 40 (39.6) 17 (16.8) 0.440 128 (63.4) 74 (36.6) 0.566 1.11 0.78 1.58 >12 months 51 (34.2) 72 (48.3) 26 (17.4) 0.130 174 (58.4) 124 (41.6) 0.048* 1.37 1.00 1.86

£12 months 53 (36.0) 73 (49.7) 21 (14.3) 0.350 179 (60.9) 115 (39.1) 0.191 1.23 0.90 1.69 rs1573954 TT TC CC Pa T C Pb OR 95% CI Controls 48 (24.6) 105 (53.9) 42 (21.5) 201 (51.5) 189 (48.5) Unilateral 13 (18.8) 31 (44.9) 25 (36.2) 0.046* 57 (41.3) 81 (58.7) 0.039* 1.51 1.02 2.24 Bilateral 45 (19.7) 116 (50.9) 67 (29.4) 0.082 206 (45.2) 250 (54.8) 0.065 1.29 0.98 1.69 Stage II 12 (16.2) 31 (41.9) 31 (41.9) 0.005* 55 (37.2) 93 (62.8) 0.003* 1.80 1.22 2.65 Stage III 18 (15.0) 69 (57.5) 33 (27.5) 0.010* 105 (43.8) 135 (56.3) 0.058 1.37 0.99 1.89 Stage IV 28 (27.2) 47 (45.6) 28 (27.2) 0.440 103 (50.0) 103 (50.0) 0.721 1.06 0.76 1.49 >12 months 30 (19.9) 76 (50.3) 45 (29.8) 0.130 136 (45.0) 166 (55.0) 0.090 1.30 0.96 1.75

£12 months 28 (19.2) 71 (48.6) 47 (32.2) 0.084 127 (43.5) 165 (56.5) 0.037* 1.38 1.02 1.87

* P<0.05 indicates statistical significance.a P values were calculated by Wald test adjusted for age; b P was calculated by Pearson chi- squared test. Bold values indicate a significant difference.

a susceptibility locus (rs10895322) for 11q-deletion neuro- and neuroblastoma may be mediated through a comparable blastoma, and 11q deletion is an inversely correlated prog- angiogenesis process that is regulated by MMP20. nostic factor in neuroblastoma [22]. In 2015, another GWAS identified association between rs10895322 and neovascular In a family-based association study, Jeremias et al. [23] lesion size in age-related macular degeneration (AMD), which concluded that rs1711399 and rs1711423 were associated suggests the possibility of using MMP20 as a novel target to with the susceptibility to molar-incisor hypomineralization control the growth of choroidal neovascularization (CNV) af- (MIH). Our study showed that rs1711399 played a risk-height- ter the onset of AMD [16]. Similarly, in this study, we found ening role in the occurrence of alcohol-induced ONFH, while that rs10895322 may be a high-risk factor of alcohol-induced rs1711423 may be a protective SNP. We hold the opinion that ONFH, implying that the risk attributed to rs10895322 in ONFH there are genetic differences between the 2 diseases.

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Figure 1. Haplotype block map for SNPs of the MMP20 gene. rs2292730 rs1711433 rs2245803 rs1573954 rs1784418 rs7126560 rs1784410 rs1711399 rs1711437 rs1784424 rs3781788 rs1711423 rs17174327 rs10895322 Block 1 (7 kb) Block 2 (20 kb) 123456 7891011121314 18 91 92 87 19 98 86 92 99 69 85 18 92 97 87 90 70 58 89 91 86 4 87 50 97 89 70 8 84 66 82 91 94 2 96 66 72 5 83 66 87 34 96 49 5 95 69 9 50 64 8

Table 5. The haplotype frequencies of MMP20 polymorphisms and their association with alcohol-induced ONFH risk.

Haplotype ORb Freq Pb rs rs rs rs rs rs rs rs (95% CI) 1711437 10895322 1784424 3781788 1711423 17174327 1784418 7126560

1 T A T C G C T G 0.377 1 –

2 C G G T T C C A 0.278 1.63 (1.15–2.30) 0.006*

3 C A G C T C C G 0.088 1.68 (1.00–2.82) 0.051

4 C A G T T C C A 0.074 0.89 (0.51–1.53) 0.670

5 C A G C T T C G 0.062 1.14 (0.63–2.07) 0.670

6 C A T C G C T G 0.055 0.79 (0.44–1.43) 0.440

* P<0.05 indicates statistical significance.b Adjusted by age. Bold values indicate a significant difference.

In our study, rs1784418 was also a protective SNP concern- between polymorphisms of rs1711437 (G>A) and kidney trans- ing the occurrence of alcohol-induced ONFH in both dominant plantation outcomes in 235 transplant recipients. The con- and log-additive genetic models. In 2012, Tannure et al. [24] clusions suggest that MMP20 polymorphisms may influence speculated that rs1784418 C>T may be involved with caries the long-term outcome of kidney allografts. Beyond that, the susceptibility in primarily Caucasian patients with poor oral MMP20 gene also has been shown to be related to kidney ag- health habits. A similar study by Filho et al. in 2016 explored ing by Wheeler et al. [28]. They showed that the A allele at the association of rs1784418 C>T and dental caries experience. rs1711437 was associated with high glomerular filtration rate Their conclusion is that the rs1784418 T allele can potentially (GFR). However, our study found no relationship between this be used as a marker for lower caries risk in certain popula- locus and alcohol-induced ONFH. The study by Wang et al. [29] tions [25]. The latest report from Brazil by Antunes et al. [26] in 2015 suggested that MMP20 rs2292730, rs12278250, and suggested that MMP20 rs1784418 C>T may contribute to the rs9787933 might be associated with ovarian cancer risk in occurrence of white spot lesions in the primary dentition. The a group of 417 ovarian cancer cases and 417 controls. In con- aforementioned conclusions are consistent with our results; trast to this conclusion, a comprehensive meta-analysis by Zhu though the function of rs1784418 is unknown, there is a pos- and Sun [30] in 2017 found that these 3 loci might not be as- sibility that the variant affects the transcription of MMP20. sociated with ovarian cancer risk. Our present study only in- In the study by Han et al. [27], they analyzed the correlations dicated that rs2292730 is a risk SNP of alcohol-induced ONFH

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among these 3 SNPs. In the study by Kanna et al. [31], genetic Conclusions polymorphisms of MMP20 (rs17099008) were significantly as- sociated with Modic changes, which are vertebral endplate We demonstrated in this study that MMP20 polymorphisms signal changes predominantly observed in the lumbar spine. can play both risk-heightening and risk-mitigating roles and Our study did not include this site. It is necessary to conduct that MMP20 may serve as a marker to predict the occurrence of larger-scale, multicenter, and high-quality studies in the future. alcohol-induced ONFH. These findings were in agreement with the multifactorial etiology of alcohol-induced ONFH. However, Up to now, the other 4 high-risk sites (rs1784424, rs3781788, because of the lack of functional studies and the fact that most rs7126560, and rs1573954) have not been described in other intronic SNPs are nonfunctional, these results more likely sug- diseases. Collectively, our study provides insight into the patho- gest that other coding SNP(s) tagged by these positive SNPs genesis of alcohol-induced ONFH. Although this study had an might be causal to alcohol-induced ONFH. adequately sufficient statistical power, some intrinsic limita- tions may still exist. First, our study did not perform a func- Acknowledgements tional study of these SNPs. To clarify the function of MMP20 in alcohol-induced ONFH, SNPs functional study is essential. We are also grateful to all participants for providing blood Second, the sample capacity was relatively small; a larger sam- samples. ple capacity will be more persuasive. Finally, participants’ eth- nicity was limited to the Han Chinese population. Hence, the Conflict of interest associations identified in this study should be confirmed in further studies with other ethnic groups. None.

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